Fluctuation of Acquired Resistance Mutations and Re-Challenge with EGFR TKI in Metastatic NSCLC: A Case Report
Bibliographic record
Abstract
Osimertinib has become the preferred first-line therapy for epidermal growth factor receptor (EGFR) mutation-positive metastatic non-small cell lung cancer (NSCLC) in recent years. Originally, it was approved for second-line treatment after epidermal growth factor receptor EGFR tyrosine kinase inhibitors (TKIs) of the first and second generations had failed and EGFR T790M had emerged as a mode of resistance. Osimertinib itself provokes a wide array of on- and off-target molecular alterations that can limit therapeutic success. Liquid biopsy ctDNA (circulating tumor DNA) analysis by hybrid capture (HC) next-generation sequencing (NGS) can help to identify alterations in a minimally invasive way and allows for the detection of common as well as rare resistance alterations. We describe a young female patient who was initially diagnosed with metastatic EGFR L858R-positive NSCLC. She received EGFR TKI therapy at different timepoints during the course of the disease and developed sequential EGFR resistance alterations (EGFR T790M and C797S). In the course of her disease, resistance alteration became undetectable, and the tumor was successfully rechallenged with the original first-generation EGFR TKI as well as osimertinib and altogether showed prolonged response despite a prognostically negative TP53 alteration. To date, the patient has been alive for more than seven years, though initially diagnosed with a heavy metastatic burden.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".